Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations891
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory90.6 KiB
Average record size in memory104.1 B

Variable types

Numeric6
Categorical5
Text2

Alerts

Age is highly overall correlated with GenderclassHigh correlation
Fare is highly overall correlated with family_sizeHigh correlation
Genderclass is highly overall correlated with Age and 2 other fieldsHigh correlation
Parch is highly overall correlated with family_sizeHigh correlation
Sex is highly overall correlated with Genderclass and 1 other fieldsHigh correlation
SibSp is highly overall correlated with family_sizeHigh correlation
Survived is highly overall correlated with Genderclass and 1 other fieldsHigh correlation
family_size is highly overall correlated with Fare and 2 other fieldsHigh correlation
PassengerId is uniformly distributed Uniform
PassengerId has unique values Unique
Name has unique values Unique
SibSp has 608 (68.2%) zeros Zeros
Parch has 678 (76.1%) zeros Zeros

Reproduction

Analysis started2024-12-17 16:40:36.223584
Analysis finished2024-12-17 16:40:52.577160
Duration16.35 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

PassengerId
Real number (ℝ)

Uniform  Unique 

Distinct891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean446
Minimum1
Maximum891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-12-17T16:40:53.861447image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45.5
Q1223.5
median446
Q3668.5
95-th percentile846.5
Maximum891
Range890
Interquartile range (IQR)445

Descriptive statistics

Standard deviation257.35384
Coefficient of variation (CV)0.57702655
Kurtosis-1.2
Mean446
Median Absolute Deviation (MAD)223
Skewness0
Sum397386
Variance66231
MonotonicityStrictly increasing
2024-12-17T16:40:55.039564image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
599 1
 
0.1%
588 1
 
0.1%
589 1
 
0.1%
590 1
 
0.1%
591 1
 
0.1%
592 1
 
0.1%
593 1
 
0.1%
594 1
 
0.1%
595 1
 
0.1%
Other values (881) 881
98.9%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
891 1
0.1%
890 1
0.1%
889 1
0.1%
888 1
0.1%
887 1
0.1%
886 1
0.1%
885 1
0.1%
884 1
0.1%
883 1
0.1%
882 1
0.1%

Survived
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
0
549 
1
342 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Length

2024-12-17T16:40:56.299991image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-17T16:40:56.776493image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Most occurring characters

ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Pclass
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
3
491 
1
216 
2
184 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Length

2024-12-17T16:40:57.297871image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-17T16:40:57.518725image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring characters

ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Name
Text

Unique 

Distinct891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2024-12-17T16:40:57.928340image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length82
Median length52
Mean length26.965208
Min length12

Characters and Unicode

Total characters24026
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique891 ?
Unique (%)100.0%

Sample

1st rowBraund, Mr. Owen Harris
2nd rowCumings, Mrs. John Bradley (Florence Briggs Thayer)
3rd rowHeikkinen, Miss. Laina
4th rowFutrelle, Mrs. Jacques Heath (Lily May Peel)
5th rowAllen, Mr. William Henry
ValueCountFrequency (%)
mr 521
 
14.4%
miss 182
 
5.0%
mrs 129
 
3.6%
william 64
 
1.8%
john 44
 
1.2%
master 40
 
1.1%
henry 35
 
1.0%
george 24
 
0.7%
james 24
 
0.7%
charles 23
 
0.6%
Other values (1515) 2538
70.0%
2024-12-17T16:40:58.771221image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24026
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24026
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24026
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2735
 
11.4%
r 1958
 
8.1%
e 1703
 
7.1%
a 1657
 
6.9%
i 1325
 
5.5%
n 1304
 
5.4%
s 1297
 
5.4%
M 1128
 
4.7%
l 1067
 
4.4%
o 1008
 
4.2%
Other values (50) 8844
36.8%

Sex
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
male
577 
female
314 

Length

Max length6
Median length4
Mean length4.704826
Min length4

Characters and Unicode

Total characters4192
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
male 577
64.8%
female 314
35.2%

Length

2024-12-17T16:40:59.135987image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-17T16:40:59.386965image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
male 577
64.8%
female 314
35.2%

Most occurring characters

ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4192
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4192
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4192
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Age
Real number (ℝ)

High correlation 

Distinct88
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.361582
Minimum0.42
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-12-17T16:40:59.653485image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile6
Q122
median28
Q335
95-th percentile54
Maximum80
Range79.58
Interquartile range (IQR)13

Descriptive statistics

Standard deviation13.019697
Coefficient of variation (CV)0.44342625
Kurtosis0.99387102
Mean29.361582
Median Absolute Deviation (MAD)6
Skewness0.51024466
Sum26161.17
Variance169.5125
MonotonicityNot monotonic
2024-12-17T16:41:00.015520image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 202
22.7%
24 30
 
3.4%
22 27
 
3.0%
18 26
 
2.9%
19 25
 
2.8%
30 25
 
2.8%
21 24
 
2.7%
25 23
 
2.6%
36 22
 
2.5%
29 20
 
2.2%
Other values (78) 467
52.4%
ValueCountFrequency (%)
0.42 1
 
0.1%
0.67 1
 
0.1%
0.75 2
 
0.2%
0.83 2
 
0.2%
0.92 1
 
0.1%
1 7
0.8%
2 10
1.1%
3 6
0.7%
4 10
1.1%
5 4
 
0.4%
ValueCountFrequency (%)
80 1
 
0.1%
74 1
 
0.1%
71 2
0.2%
70.5 1
 
0.1%
70 2
0.2%
66 1
 
0.1%
65 3
0.3%
64 2
0.2%
63 2
0.2%
62 4
0.4%

SibSp
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52300786
Minimum0
Maximum8
Zeros608
Zeros (%)68.2%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-12-17T16:41:00.287089image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1027434
Coefficient of variation (CV)2.1084644
Kurtosis17.88042
Mean0.52300786
Median Absolute Deviation (MAD)0
Skewness3.6953517
Sum466
Variance1.2160431
MonotonicityNot monotonic
2024-12-17T16:41:00.530348image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 608
68.2%
1 209
 
23.5%
2 28
 
3.1%
4 18
 
2.0%
3 16
 
1.8%
8 7
 
0.8%
5 5
 
0.6%
ValueCountFrequency (%)
0 608
68.2%
1 209
 
23.5%
2 28
 
3.1%
3 16
 
1.8%
4 18
 
2.0%
5 5
 
0.6%
8 7
 
0.8%
ValueCountFrequency (%)
8 7
 
0.8%
5 5
 
0.6%
4 18
 
2.0%
3 16
 
1.8%
2 28
 
3.1%
1 209
 
23.5%
0 608
68.2%

Parch
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38159371
Minimum0
Maximum6
Zeros678
Zeros (%)76.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-12-17T16:41:00.753634image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.80605722
Coefficient of variation (CV)2.1123441
Kurtosis9.7781252
Mean0.38159371
Median Absolute Deviation (MAD)0
Skewness2.749117
Sum340
Variance0.64972824
MonotonicityNot monotonic
2024-12-17T16:41:01.000811image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 678
76.1%
1 118
 
13.2%
2 80
 
9.0%
5 5
 
0.6%
3 5
 
0.6%
4 4
 
0.4%
6 1
 
0.1%
ValueCountFrequency (%)
0 678
76.1%
1 118
 
13.2%
2 80
 
9.0%
3 5
 
0.6%
4 4
 
0.4%
5 5
 
0.6%
6 1
 
0.1%
ValueCountFrequency (%)
6 1
 
0.1%
5 5
 
0.6%
4 4
 
0.4%
3 5
 
0.6%
2 80
 
9.0%
1 118
 
13.2%
0 678
76.1%

Ticket
Text

Distinct681
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2024-12-17T16:41:01.463831image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length18
Median length17
Mean length6.7508418
Min length3

Characters and Unicode

Total characters6015
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique547 ?
Unique (%)61.4%

Sample

1st rowA/5 21171
2nd rowPC 17599
3rd rowSTON/O2. 3101282
4th row113803
5th row373450
ValueCountFrequency (%)
pc 60
 
5.3%
c.a 27
 
2.4%
a/5 17
 
1.5%
ca 14
 
1.2%
ston/o 12
 
1.1%
2 12
 
1.1%
sc/paris 9
 
0.8%
w./c 9
 
0.8%
soton/o.q 8
 
0.7%
347082 7
 
0.6%
Other values (709) 955
84.5%
2024-12-17T16:41:02.308540image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6015
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6015
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6015
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 746
12.4%
1 689
11.5%
2 594
9.9%
7 490
8.1%
4 464
 
7.7%
6 422
 
7.0%
0 406
 
6.7%
5 387
 
6.4%
9 328
 
5.5%
8 282
 
4.7%
Other values (25) 1207
20.1%

Fare
Real number (ℝ)

High correlation 

Distinct247
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.447545
Minimum4.0125
Maximum512.3292
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-12-17T16:41:02.668714image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum4.0125
5-th percentile7.2292
Q17.925
median14.4542
Q331
95-th percentile112.07915
Maximum512.3292
Range508.3167
Interquartile range (IQR)23.075

Descriptive statistics

Standard deviation49.570235
Coefficient of variation (CV)1.5277037
Kurtosis33.663618
Mean32.447545
Median Absolute Deviation (MAD)6.7167
Skewness4.8121943
Sum28910.762
Variance2457.2082
MonotonicityNot monotonic
2024-12-17T16:41:02.994900image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.05 43
 
4.8%
13 42
 
4.7%
7.8958 38
 
4.3%
7.75 34
 
3.8%
26 31
 
3.5%
10.5 24
 
2.7%
14.4542 22
 
2.5%
7.925 18
 
2.0%
7.775 16
 
1.8%
7.2292 15
 
1.7%
Other values (237) 608
68.2%
ValueCountFrequency (%)
4.0125 1
0.1%
5 1
0.1%
6.2375 1
0.1%
6.4375 1
0.1%
6.45 1
0.1%
6.4958 2
0.2%
6.75 2
0.2%
6.8583 1
0.1%
6.95 1
0.1%
6.975 2
0.2%
ValueCountFrequency (%)
512.3292 3
0.3%
263 4
0.4%
262.375 2
0.2%
247.5208 2
0.2%
227.525 4
0.4%
221.7792 1
 
0.1%
211.5 1
 
0.1%
211.3375 3
0.3%
164.8667 2
0.2%
153.4625 3
0.3%

Embarked
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
S
646 
C
168 
Q
77 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowC
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 646
72.5%
C 168
 
18.9%
Q 77
 
8.6%

Length

2024-12-17T16:41:03.287902image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-17T16:41:03.523557image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
s 646
72.5%
c 168
 
18.9%
q 77
 
8.6%

Most occurring characters

ValueCountFrequency (%)
S 646
72.5%
C 168
 
18.9%
Q 77
 
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 646
72.5%
C 168
 
18.9%
Q 77
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 646
72.5%
C 168
 
18.9%
Q 77
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 646
72.5%
C 168
 
18.9%
Q 77
 
8.6%

family_size
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9046016
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-12-17T16:41:03.750206image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile6
Maximum11
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6134585
Coefficient of variation (CV)0.84713704
Kurtosis9.159666
Mean1.9046016
Median Absolute Deviation (MAD)0
Skewness2.7274415
Sum1697
Variance2.6032485
MonotonicityNot monotonic
2024-12-17T16:41:03.994740image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 537
60.3%
2 161
 
18.1%
3 102
 
11.4%
4 29
 
3.3%
6 22
 
2.5%
5 15
 
1.7%
7 12
 
1.3%
11 7
 
0.8%
8 6
 
0.7%
ValueCountFrequency (%)
1 537
60.3%
2 161
 
18.1%
3 102
 
11.4%
4 29
 
3.3%
5 15
 
1.7%
6 22
 
2.5%
7 12
 
1.3%
8 6
 
0.7%
11 7
 
0.8%
ValueCountFrequency (%)
11 7
 
0.8%
8 6
 
0.7%
7 12
 
1.3%
6 22
 
2.5%
5 15
 
1.7%
4 29
 
3.3%
3 102
 
11.4%
2 161
 
18.1%
1 537
60.3%

Genderclass
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
male
538 
female
275 
child
78 

Length

Max length6
Median length4
Mean length4.704826
Min length4

Characters and Unicode

Total characters4192
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
male 538
60.4%
female 275
30.9%
child 78
 
8.8%

Length

2024-12-17T16:41:04.300447image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-17T16:41:04.560063image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
male 538
60.4%
female 275
30.9%
child 78
 
8.8%

Most occurring characters

ValueCountFrequency (%)
e 1088
26.0%
l 891
21.3%
m 813
19.4%
a 813
19.4%
f 275
 
6.6%
c 78
 
1.9%
h 78
 
1.9%
i 78
 
1.9%
d 78
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4192
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1088
26.0%
l 891
21.3%
m 813
19.4%
a 813
19.4%
f 275
 
6.6%
c 78
 
1.9%
h 78
 
1.9%
i 78
 
1.9%
d 78
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4192
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1088
26.0%
l 891
21.3%
m 813
19.4%
a 813
19.4%
f 275
 
6.6%
c 78
 
1.9%
h 78
 
1.9%
i 78
 
1.9%
d 78
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4192
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1088
26.0%
l 891
21.3%
m 813
19.4%
a 813
19.4%
f 275
 
6.6%
c 78
 
1.9%
h 78
 
1.9%
i 78
 
1.9%
d 78
 
1.9%

Interactions

2024-12-17T16:40:48.239913image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:37.033105image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:38.487107image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:40.641072image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:43.315159image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:45.725454image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:48.742382image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:37.250576image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:38.827377image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:40.973760image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:43.711138image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:46.151357image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:49.478064image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:37.506821image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:39.228143image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:41.384526image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:44.199705image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:46.521578image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:49.992708image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:37.736264image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:39.644975image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:41.962850image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:44.582612image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:46.905281image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:50.371220image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:37.977283image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:39.978297image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:42.458047image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:44.956360image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:47.364623image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:50.686291image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:38.222121image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:40.292308image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:42.832785image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:45.370053image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-17T16:40:47.854200image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-12-17T16:41:05.012268image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
AgeEmbarkedFareGenderclassParchPassengerIdPclassSexSibSpSurvivedfamily_size
Age1.0000.1510.1360.645-0.2170.0350.2650.106-0.1450.158-0.183
Embarked0.1511.0000.2060.0820.0520.0000.2580.1110.0920.1640.083
Fare0.1360.2061.0000.1590.407-0.0070.4350.1910.4420.2710.524
Genderclass0.6450.0820.1591.0000.3900.0560.1360.9500.3840.5660.381
Parch-0.2170.0520.4070.3901.0000.0010.0220.2470.4500.1570.801
PassengerId0.0350.000-0.0070.0560.0011.0000.0320.066-0.0610.104-0.050
Pclass0.2650.2580.4350.1360.0220.0321.0000.1300.1480.3370.137
Sex0.1060.1110.1910.9500.2470.0660.1301.0000.2060.5400.205
SibSp-0.1450.0920.4420.3840.450-0.0610.1480.2061.0000.1870.849
Survived0.1580.1640.2710.5660.1570.1040.3370.5400.1871.0000.215
family_size-0.1830.0830.5240.3810.801-0.0500.1370.2050.8490.2151.000

Missing values

2024-12-17T16:40:51.166895image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-17T16:40:52.102801image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareEmbarkedfamily_sizeGenderclass
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500S2male
1211Cumings, Mrs. John Bradley (Florence Briggs Thayer)female38.010PC 1759971.2833C2female
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250S1female
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000S2female
4503Allen, Mr. William Henrymale35.0003734508.0500S1male
5603Moran, Mr. Jamesmale28.0003308778.4583Q1male
6701McCarthy, Mr. Timothy Jmale54.0001746351.8625S1male
7803Palsson, Master. Gosta Leonardmale2.03134990921.0750S5child
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333S3female
91012Nasser, Mrs. Nicholas (Adele Achem)female14.01023773630.0708C2child
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareEmbarkedfamily_sizeGenderclass
88188203Markun, Mr. Johannmale33.0003492577.8958S1male
88288303Dahlberg, Miss. Gerda Ulrikafemale22.000755210.5167S1female
88388402Banfield, Mr. Frederick Jamesmale28.000C.A./SOTON 3406810.5000S1male
88488503Sutehall, Mr. Henry Jrmale25.000SOTON/OQ 3920767.0500S1male
88588603Rice, Mrs. William (Margaret Norton)female39.00538265229.1250Q6female
88688702Montvila, Rev. Juozasmale27.00021153613.0000S1male
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000S1female
88888903Johnston, Miss. Catherine Helen "Carrie"female28.012W./C. 660723.4500S4female
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C1male
89089103Dooley, Mr. Patrickmale32.0003703767.7500Q1male